Standard global image-based similarity measures (e.g., Correlation, SSD, Mutual Information, as described in the article by P. Viola and W. Wells, III. Alignment by maximization of mutual information. In ICCV95, pages 16-23, etc.) require prior alignment or prior knowledge of dense correspondences between signals. There are also distance measures that are based on comparing empirical distributions of local image features, such as “bags of features” (e.g., the article by J. Sivic, B. Russell, A. Efros, A. Zisserman, and W. Freeman. Discovering objects and their localization in images. In ICCV05, pages I: 370-377).
Attempts to maintain the benefits of local methods (such as the “bags of features” method) while maintaining global structural information have recently been proposed by S. Lazebnik, C. Schmid, and J. Ponce in the article Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR06, Volume: 2, pp. 2169-2178. This was done by partitioning the image into increasingly fine subregions, each region with its own feature histogram. These have been shown to improve upon the “bags of features” method, but are restricted to a preselected partitioning of the image into rectangular regions.
The article by O. Boiman and M. Irani (Detecting irregularities in images and in video, in ICCV05, pages I: 462-469), which article is incorporated herein by reference, describes a method for detecting irregularities in images/video as regions that cannot be composed from large pieces of data of other images/video. That approach was restricted to detecting local irregularities.
Biologists utilize large shared regions between signals to determine similarities between DNA sequences, amino acid chains, etc. Tools for finding large repetitions in biological data have been developed (e.g., “BLAST” by S. Altschul, W. Gish, W. Miller, E. Myers, and D. Lipman. Basic local alignment search tool. J Mol Biol, 215:403-410, 1990).